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使用第一性原理计算和卷积神经网络算法理解锌掺杂羟基磷灰石结构。

Understanding zinc-doped hydroxyapatite structures using first-principles calculations and convolutional neural network algorithm.

机构信息

MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, P. R. China.

Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan, 610065, P. R. China.

出版信息

J Mater Chem B. 2022 Feb 23;10(8):1281-1290. doi: 10.1039/d1tb02687a.

DOI:10.1039/d1tb02687a
PMID:35133393
Abstract

Element doping is widely used to improve the performance of materials by changing their intrinsic properties. However, the lack of direct crystallographic structures for dopants has restricted the effective high-throughput design or refinement of materials using the doping strategy. Herein, Zn-doped hydroxyapatite (HAP) was selected as the template material. The first-principles optimization and machine learning algorithm were combined to understand the mechanism of HAP doped with Zn. Our method could effectively locate the structures in the lowest-energy region. Specifically, our results indicate that the first Zn atom showed a tendency to occupy the Ca(II) site first, and the subsequent Zn atoms entering the Ca(II) sites in a symmetrical manner. The symmetrical entrance of Zn atoms to HAP would minimize the interaction energy between the Zn atoms and the degree of crystal deformation. Finally, we performed uniaxial stretching simulations to evaluate the influence of Zn ions on the mechanical behavior of HAP based on the optimally-doped structure obtained by machine learning (ML). The calculation results were consistent with the experimental conclusion that the doping of Zn ions could improve the fracture toughness of HAP. Our work would provide an effective way to locate possible optimized structures in the doping system and subsequently improve the design of materials.

摘要

元素掺杂被广泛用于通过改变材料的固有性质来提高其性能。然而,掺杂剂缺乏直接的晶体结构限制了使用掺杂策略对材料进行有效高通量设计或优化。在此,选择锌掺杂羟基磷灰石(HAP)作为模板材料。我们将第一性原理优化和机器学习算法相结合,以了解 HAP 中掺杂 Zn 的机制。我们的方法可以有效地定位到能量最低的区域中的结构。具体来说,我们的结果表明,第一个 Zn 原子首先倾向于占据 Ca(II)位,随后的 Zn 原子以对称的方式进入 Ca(II)位。Zn 原子以对称的方式进入 HAP 将最小化 Zn 原子之间的相互作用能和晶体变形程度。最后,我们根据机器学习(ML)获得的最佳掺杂结构进行了单轴拉伸模拟,以评估 Zn 离子对 HAP 力学性能的影响。计算结果与实验结论一致,即掺杂 Zn 离子可以提高 HAP 的断裂韧性。我们的工作将为在掺杂体系中定位可能的优化结构提供一种有效方法,并进而改进材料的设计。

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